The economics of solar will bulldoze past any need for subsidies from the government.
5,477 karma · joined July 24, 2014
abetusk@mechaelephant.com
Let's say you're a company that's providing an internet connection to a business. The company trusts you, so there's only compression of bits over the wire, not encryption, and you're aware of the compression scheme the company is using to send their bits to you. You're charging the company a premium for using the line you manage but you also lease the line, so it's in your interest to compress what they give you as best as possible so as to make a profit.
Say the companies compression scheme is imperfect. They have a Huffman coding of their (imperfect) model of tokens they send, call it q(x) (that is, they think token x shows up with probability q(x)). You've determined the true distribution, p(x) (token x shows up with actual probability p(x)).
The business has tokens that show up with probability p(x) but they encode them with lg(q(x)) bits, giving an average token bit size of:
-\sum _ x p(x) lg(q(x))
-\sum _ x p(x) lg(p(x))
-\sum _ x p(x) lg(p(x)) - \sum _ x p(x) lg(q(x)) = -\sum _ x p(x) lg(p(x)/q(x))
To me, this is a much more intuitive explanation. I made a blog post about it [0], if anyone cares.
[0] https://mechaelephant.com/dev/Kullback-Leibler-Divergence.ht...
Spending on social security is about $680B, medicare is $480B, defense is at $410B [1].
Microsoft's valuation is approx. $2.8T [2], Google $3.8T [3], Amazon $2.3T [4], Facebook $1.6T [5] (Linux supply side is valued at approx. $8.8T).
The FBI employs roughly 38k people with about $10B in funding [7]. The CIA employs roughly 22k people with about $15B (?) in funding [8].
So, from that perspective, $10B is roughly .5% of yearly tax revenue (and about how much the FBI/CIA are funded) and estimated 50k people is about the size of the FBI and CIA combined.
[0] https://fiscaldata.treasury.gov/americas-finance-guide/gover...
[1] https://fiscaldata.treasury.gov/americas-finance-guide/feder...
[2] https://finance.yahoo.com/quote/MSFT/key-statistics/
[3] https://finance.yahoo.com/quote/GOOG/key-statistics/
[4] https://companiesmarketcap.com/amazon/marketcap/
[5] https://companiesmarketcap.com/meta-platforms/marketcap/
[6] https://www.hbs.edu/ris/Publication%20Files/24-038_51f8444f-...
[7] https://en.wikipedia.org/wiki/Federal_Bureau_of_Investigatio...
[8] https://en.wikipedia.org/wiki/Central_Intelligence_Agency
[0] https://www.crowdsupply.com/oddly-specific-objects/sensor-wa...
[1] https://www.crowdsupply.com/oddly-specific-objects/sensor-wa...
Instead of using Palantir, working at the FSF, the Linux Foundation, etc. It's not that they don't make good money, it's that it's often a fraction of what could be made at a comparable for profit company.
I think the video game industry is an apt comparison. The pay is often not very good with the motivation being, for many people, prestige based, in some form or another. I suspect there are analogies in the game industry and publishing 50-100 years ago.
b) true there is no breakdown but I would expect the exact opposite as fields get harder. More context requires more training and familiarity, which I would expect to increase age.
My point is that I think there's a bias in the field towards the youth narrative but the majority of discovery, even in physics, happens at a later age.
My take on this is that it takes about a decade before experience, knowledge and wisdom can be used to see a bigger picture to make a breakthrough.
[0] https://priceonomics.com/at-what-age-does-genius-strike/
https://www.officialdata.org/us/inflation/1800?amount=1
Doing a spot check, this means $1 in in 1913 is equivalent to roughly $32.83 today.
Wikipedia has it at #9FE2BF [0].
[0] https://en.wikipedia.org/wiki/Spring_green#Sea_foam_green
For example, stability of dithering under rotation and or some type of shear translation. What about stability under scaling?
There's been some other methods that essentially create a dither texture on the surface itself but, to me at least, this has a different quality than the "screen space" dithering that Obra Dinn employs.
Does anyone have any ideas on how to make this idea more rigorous? Or is the set of assumption fundamentally contradictory?
If the probability distribution converges, it converges to a Levy stable distribution [0].
If sums of independent identically distributed random variables converge to a distribution, they converge to a Levy stable distribution [0]. Tails of the Levy stable distribution are power law, which makes them not Gaussian.
If I had made the extra condition that the random variables had finite variance, you'd be correct. Without the finite variance condition, the distribution is Levy stable.
Levy stable distributions can have finite mean but infinite variance. They can also have infinite mean and infinite variance. Only in the finite mean and finite variance case does it imply a Gaussian.
Levy stable distributions are also called "fat-tailed", "heavy-tailed" or "power law" distributions. In some sense, Levy stable distributions are more normal than the normal distribution. It might be tempting to dismiss the infinite variance condition but, practically, this just means you get larger and larger numbers as you draw from the distribution.
This was one of Mandelbrot's main positions, that power laws were much more common than previously thought and should be adopted much more readily.
As an aside, if you do ever get asked this in an interview, don't expect to get the job if you answer correctly.
The sum of independent identically distributed random variables, if they converge at all, converge to a Levy stable distribution (aka fat-tailed, heavy tailed, power law). In this sense, Levy stable distributions are more "normal" than the normal distribution. They also show up with regular frequency all over nature.
As you point out, infinite variance might be dismissed but, in practice, this just ends up getting larger and larger "outliers" as one keeps drawing from the distribution. Infinities are, in effect, a "verb" and so an infinite variance, in this context, just means the distributions spits out larger and larger numbers the more you sample from it.
Widths of different uniform distributions along with different centers all still have a quadratic center, so the above argument only needs to be minimally changed.
The added bonus is that if the (1-w^2)^n is replaced by (1-w^a)^n, you can sort of see how to get at the Levy stable distribution (see the characteristic function definition [0]).
The point is that this gives a simple, high-level motivation as to why it's so common. Aside from seeing this flavor of proof in "An Invitation to Modern Number Theory" [1], I haven't really seen it elsewhere (though, to be fair, I'm not a mathematician). I also have never heard the connection of this method to the Levy stable distributions but for someone communicating it to me personally.
I disagree about the audience for Quanta. They tend to be exposed to higher level concepts even if they don't have a lot of in depth experience with them.
[0] https://en.wikipedia.org/wiki/Stable_distribution#Parametriz...
[1] https://www.amazon.com/Invitation-Modern-Number-Theory/dp/06...
For simplicity, take N identically distributed random variables that are uniform on the interval from [-1/2,1/2], so the probability distribution function, f(x), on the interval from [-1/2,1/2] is 1.
The Fourier transform of f(x), F(w), is essentially sin(w)/w. Taking only the first few terms of the Taylor expansion, ignoring constants, gives (1-w^2).
Convolution is multiplication in Fourier space, so you get (1-w^2)^n. Squinting, (1-w^2)^n ~ (1-n w^2 / n)^n ~ exp(-n w^2). The Fourier transform of a Gaussian is a Gaussian, so the result holds.
Unfortunately I haven't worked it out myself but I've been told if you fiddle with the exponent of 2 (presumably choosing it to be in the range of (0,2]), this gives the motivation for Levy stable distributions, which is another way to see why fat-tailed/Levy stable distributions are so ubiquitous.
You're right. My proposed solution only addressed AI at scale (web crawlers, mass spam campaigns, etc.) and doesn't address low bandwidth, "high friction" events like PRs, code reviews, blog posts, etc.
I don't want to dismiss the concerns outright and I'm not sure I have a concise response but my feeling is that, in some sense, it doesn't really matter. If AI is used to create a high quality output, then it should be accepted. If AI is creating low quality output, then it should be easy to verify, maybe with better (AI) tooling.
In other words, the bot problem cannot be solved, in that we might never know whether the source is from human or machine, but it won't matter as that's not the core of the problem, quality content is.
My opinion is that DIT is overstated and, where it isn't, we'll see much better technology evolve to separate the signal from the noise. As an analogy, in the late 1990s, internet search engines were abysmal, raking by document keyword searches and so were easily game-able by content that had nothing to do with the search intent. Google came along with page rank and, almost overnight, made the internet usable. From the bad Yahoo search results, one might be tempted to think that the entirety of the internet looked like what Yahoo was serving, but this was the wrong impression as there were plenty of interesting things on the internet, it just took page rank to provide the necessary filter to make the internet usable.
Anubis is one such answer [0]. Cryptocurrency and micro transactions are another.
In the last few decades, spam was a problem because the marginal transaction costs of information exchange were orders of magnitude lower than they had been. Note that physical mail spam was, and still, is an issue. Focusing on perceptual or fuzzy computation as the limiting factor, through captchas and other 'human tests', allowed for most spam to be effectively mitigated.
Now that intelligence is becoming orders of magnitude cheaper, perceptual computation challenges no longer work, but we can still do computation challenges in the form of proof of work or proxies thereof. Spam will never wholly go away but we can at least cause more friction by charging bot networks to execute in the form of energy or money.
But this isn't actually recreating murmurations, is it? This is a neural network that's using the Reynolds criteria as a loss function, with Cavagna's topological neighbors?
As far as I know, there's no good research that reproduces the murmations seen in starling flocks. This seems like it would be a good use case for neural networks but I don't know of any publicly available 3d data of actual starling flocks, aside from some random YouTube videos floating around.
Software development is a highly complex task and verification becomes not just validation of the output but also verification that the work is solving the problem desired, not just the problem specified.
I'm empathetic to that scenario, but this was a problem with software development to begin with. I would much rather be in a situation of reducing friction to verification than reducing friction to discovery.
Cognitive load might be the same but now we get a potential boost in productivity for the same cost.
https://dresdencodak.com/2009/09/22/caveman-science-fiction/
> ... Is there some edifying lesson we can salvage from the wreckage? ... One obvious lesson is to stay away from brand. ... Sure, you might be able to make money this way ... but pushing people's brand buttons is just not a good problem to work on ...
> Go where interesting problems are, and you'll probably find that other smart and ambitious people have turned up there too. And later they'll look back on what you did together and call it a golden age.
I do feel like RSS feeds are one of the easier things to do DIY, custom to people's specific taste of how to list data of this sort. All the 'off the shelf' RSS feeds that I see feel contrived, cluttered and bloated.